Solution Architecture Overview

On-Chain Data: Real-time monitoring of over 3,000 smart contract interactions using The Graph indexer.
Market Data: Aggregation of depth order books from 15 major exchanges, including Binance and Coinbase.
Social Data: A distributed web crawler network analyzes more than 200,000 social media discussions per hour, covering platforms such as Twitter, Reddit, and Telegram.

Feature Engineering: Leveraging Apache Spark for real-time, minute-level feature extraction from multi-source data streams.

A parallel ensemble of models, including:

LSTM networks for time-series prediction
Random Forest classifiers for discrete feature processing
Custom Attention Mechanism for identifying high-impact events across data modalities
Federated Learning: Models are trained locally on user devices, transmitting only encrypted gradients to ensure privacy and security while enabling personalized model refinement.

Prediction Integrity: Prediction outputs are hashed and stored via IPFS, with timestamp anchoring on the Polygon blockchain for transparency and traceability.
Tamper-Proof Validation: Employing zk-SNARKs (zero-knowledge proofs) to cryptographically verify that predictions have not been altered.
Smart Trade Execution: On-chain smart contracts automatically route user trades to the optimal decentralized exchange (DEX) based on liquidity and slippage analysis.

Last updated 3 days ago

On-Chain Data: Real-time monitoring of over 3,000 smart contract interactions using The Graph indexer.
Market Data: Aggregation of depth order books from 15 major exchanges, including Binance and Coinbase.
Social Data: A distributed web crawler network analyzes more than 200,000 social media discussions per hour, covering platforms such as Twitter, Reddit, and Telegram.

Feature Engineering: Leveraging Apache Spark for real-time, minute-level feature extraction from multi-source data streams.

A parallel ensemble of models, including:

LSTM networks for time-series prediction
Random Forest classifiers for discrete feature processing
Custom Attention Mechanism for identifying high-impact events across data modalities
Federated Learning: Models are trained locally on user devices, transmitting only encrypted gradients to ensure privacy and security while enabling personalized model refinement.

Prediction Integrity: Prediction outputs are hashed and stored via IPFS, with timestamp anchoring on the Polygon blockchain for transparency and traceability.
Tamper-Proof Validation: Employing zk-SNARKs (zero-knowledge proofs) to cryptographically verify that predictions have not been altered.
Smart Trade Execution: On-chain smart contracts automatically route user trades to the optimal decentralized exchange (DEX) based on liquidity and slippage analysis.

Last updated 3 days ago